Noise suppression devices have significant applications in the enhancement of narrowband spectral lines in a broadband noise field when there is a poor signal-to-noise ratio at the input and where there is insufficient a prior information on which to design appropriate filters. The device automatically filters out the components of the signal which are uncorrelated in time and passes the correlated portions. Since the properties of the device are determined solely by the input signal statistics, the properties of the filter automatically adjust to variations in the input signal statistics to obtain the least means square (LMS) approximation to a Wiener-Hopf filter. The device will thus track slowly varying spectral lines in broadband noise.
Time-domain and frequency-domain adaptive filtering techniques have been utilized with varying degrees of success to filter background noise from a desired signal, e.g., see references 1-12. While the signal can be any desired signal embedded in background noise, representative examples are speech or a signal signature indicative of the operation of machinery. Prior filtering algorithms have, however, failed to take into account the effects of circular convolutions on the filtered output signal. In speech signals, for example, such effects produce speech that sounds tinny, containing higher frequency components resulting from aliasing. In general, failure to account for the effects of circular convolutions results in noise contamination and/or signal diminution.
Noise suppression as well as feedback suppression devices have also generally failed to recognize the importance of a vector weight parameter for maintaining spectrum fidelity during signal processing. High fidelity is important in all types of signal communication, detection and identification.